模型详情：INT8 Whisper large v2

Whisper 是一个用于自动语音识别 (ASR) 和语音翻译的预训练模型。该模型在 680k 小时的标注数据上训练，Whisper 模型展现了强大的泛化能力，无需微调即可适应多种数据集和领域。

此 int8 ONNX 模型由 neural-compressor 生成，fp32 模型可通过以下命令导出：

optimum-cli export onnx --model openai/whisper-large-v2 whisper-large-v2-with-past/ --task automatic-speech-recognition-with-past --opset 13

如何使用

通过克隆仓库下载模型：

git clone https://huggingface.co/Intel/whisper-large-v2-int8-dynamic

使用以下代码评估模型：

import os
from evaluate import load
from datasets import load_dataset
from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig
model_name = 'openai/whisper-large-v2'
model_path = 'whisper-large-v2-int8-dynamic'
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
wer = load(\"wer\")
librispeech_test_clean = load_dataset(\"librispeech_asr\", \"clean\", split=\"test\")
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import PretrainedConfig
model_config = PretrainedConfig.from_pretrained(model_name)
predictions = []
references = []
sessions = ORTModelForSpeechSeq2Seq.load_model(
            os.path.join(model_path, 'encoder_model.onnx'),
            os.path.join(model_path, 'decoder_model.onnx'),
            os.path.join(model_path, 'decoder_with_past_model.onnx'))
model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2])
for idx, batch in enumerate(librispeech_test_clean):
    audio = batch[\"audio\"]
    input_features = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"], return_tensors=\"pt\").input_features
    reference = processor.tokenizer._normalize(batch['text'])
    references.append(reference)
    predicted_ids = model.generate(input_features)[0]
    transcription = processor.decode(predicted_ids)
    prediction = processor.tokenizer._normalize(transcription)
    predictions.append(prediction)
wer_result = wer.compute(references=references, predictions=predictions)
print(f\"Result wer: {wer_result * 100}\")
accuracy = 1 - wer_result
print(\"Accuracy: %.5f\" % accuracy)

指标（模型性能）：